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The Local Power of the CUSUM and CUSUM of Squares Tests

Published online by Cambridge University Press:  11 February 2009

Werner Ploberger  and
Walter Krämer;
Werner Ploberger
Affiliation:
Technische Universität Wien
Walter Krämer;
Affiliation:
Universität Dortmund
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Abstract

We consider the local power of the cusum and cusum of squares tests for structural change in the linear regression model. We show that the local power of the cusum of squares test equals its size for a wide class of structural changes, as compared to a nontrivial local power for the cusum test. The conventional ranking of these procedures is thus reversed.

Type
Articles
Information
Econometric Theory , Volume 6 , Issue 3 , September 1990 , pp. 335 - 347
Copyright
Copyright © Cambridge University Press 1990

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References

1.Ashley, R.A simple test for regression parameter instability. Economic Inquiry 22 (1984): 253–268.CrossRefGoogle Scholar
2.Billingsley, P.Convergence of Probability Measures. New York: Wiley, 1968.Google Scholar
3.Brown, R.L., Durbin, J., & Evans, J.M.Techniques for testing the constancy of regression relationships over time. Journal of the Royal Statistical Society B 37, (1975): 149–163.Google Scholar
4.Deshayes, J. & Picard, D. Off-line statistical analysis of change point models using non-parametric and likelihood methods. In Basseville, M. & Beneviste, A. (eds.), Detection of Abrupt Changes in Signals and Dynamical Systems (Lecture Notes in Control and Information Sciences 77), 103–168, Berlin: Springer, 1986.Google Scholar
5.Garbade, K.Two methods of examining the stability of regression coefficients. Journal of the American Statistical Association 72 (1977): 54–63.CrossRefGoogle Scholar
6.Hackl, P.Testing the Constancy of Regression Models over Time, Göttingen: Vandenhock & Ruprecht, 1980.Google Scholar
7.Johnston, J.Econometric Methods (3rd ed.). New York: McGraw-Hill, 1984.Google Scholar
8.Krämer, W., Ploberger, W., & Alt, R.Testing for structural change in dynamic models. Econometrica 56 (1988): 1355–1369.CrossRefGoogle Scholar
9.Krämer, W. & Sonnberger, H.The Linear Regression Model Under Test. Heidelberg: Physica-Verlag, 1986.CrossRefGoogle Scholar
10.McCabe, B.P.M. & Harrison, M.J.Testing the constancy of regression coefficients over time using least squares residuals. Applied Statistics 29 (1980): 142–148.CrossRefGoogle Scholar
11.Meyer, P.A.Martingale and Stochastic Integrals I (Lecture Notes in Mathematics 284). Heidelberg: Springer, 1972.CrossRefGoogle Scholar
12.Neveu, J.Discrete Parameter Martingales. Amsterdam: North Holland, 1975.Google Scholar
13.Ploberger, W. The local power of the cusum-SQ test against heteroskedasticity. In Hackl, P. (ed.), Statistical Analysis and Forecasting of Economic Structural Change. Heidelberg: Springer, 1989, 127–133.CrossRefGoogle Scholar
14.Ploberger, W. & Kramer, W.On studentizing a test for structural change. Economics Letters 20 (1986): 341–344.CrossRefGoogle Scholar
15.Sen, P.K.Invariance principles for recursive residuals. The Annals of Statistics 10 (1982): 307–312.CrossRefGoogle Scholar